Jobs at Ciklum

Design and implement an end-to-end QA automation framework for Microsoft Fabric data platforms. Validate Medallion Architecture layers, pipelines, schemas, data quality, reconciliations, Power BI semantic models, reports, and AI agents. Build regression suites using SQL, Python, PySpark, Playwright, and TypeScript; investigate root causes; monitor observability metrics; configure alerts; and integrate automated checks into Azure DevOps or other CI/CD systems. Collaborate with data engineering, BI, AI, and DevOps teams.

Leads business and systems analysis for consolidating payment gateways into a target platform. Elicits and documents requirements, creates functional specifications and user stories, analyzes data and migration mappings, and supports architecture, development, testing, UAT, releases, and production issues. Provides deep U.S. payments and TSYS acquiring expertise, manages program-level dependencies and traceability, and facilitates SAFe ceremonies. Collaborates with stakeholders, architects, engineers, QA, migration teams, payment networks, and business teams while addressing compliance, processor integrations, authorization, settlement, chargebacks, and reconciliation.

Leads technical execution for a large-scale Microsoft Fabric enterprise data acquisition program. Designs scalable Lakehouse, Warehouse, OneLake, and Medallion Architecture solutions; standardizes Bronze and Silver layer onboarding through reusable ingestion frameworks; coordinates distributed engineering squads; governs metadata, lineage, data quality, monitoring, and operationalization; and partners with architects, stakeholders, modelers, governance teams, and delivery managers to ensure reliable enterprise data product delivery.

Designs and maintains scalable Playwright and TypeScript automation for responsive, data-intensive web applications. Covers functional, integration, contract, end-to-end, reliability, security, accessibility, performance, and cross-browser testing. The role validates complex UI behavior, large datasets, rendering, network performance, and responsiveness while diagnosing issues through developer tools and telemetry. It also promotes quality across the development lifecycle and uses AI-assisted tools to improve test design and maintenance.

Design and develop scalable automated tests for server-side applications, REST APIs, microservices, integrations, asynchronous workflows, and Azure-based systems. Build functional, integration, contract, end-to-end, reliability, and API security coverage using Playwright with TypeScript or C#. Integrate quality gates into CI/CD and Docker deployments, validate data contracts and event-driven workflows, and collaborate with developers to shift quality left. Use AI-assisted tools to improve test design, debugging, and refactoring.

Design and deploy enterprise GenAI and agentic workflow platforms for pharmaceutical and life sciences applications. Build multi-agent systems, RAG pipelines, AWS Bedrock integrations, governed AI workflows, Python/FastAPI microservices, React applications, databases, and cloud infrastructure. Implement security, compliance, auditability, automated testing, and production deployment across healthcare-focused systems.

Architects and deploys production-grade agentic AI and multi-agent systems using LLMs, RAG, vector databases, prompt engineering, and NLP. Designs autonomous workflows, tool calling, memory, routing, evaluation, observability, and guardrails. Builds backend APIs and microservices while implementing testing for model drift, prompt regression, and agent reliability. Requires advanced Python, hands-on production Generative AI experience, and expertise with agent orchestration frameworks and vector search.

Design, build, and support production B2B application features across React and TypeScript front ends and Java Spring Boot APIs. Modernize legacy Oracle ADF and JSF systems, collaborate with Product and Design, improve code quality and test coverage, participate in on-call and incident response, address security findings, mentor engineers, and guide technical decisions. The role also involves developing AI workflows and evaluating AI-generated code.

Lead DevOps Engineer responsible for building AWS development, testing, and production environments; creating CI/CD pipelines; managing infrastructure as code, secrets, IAM, networking, API gateways, observability, monitoring, and alerting. The role also develops runbooks, rollback plans, deployment documentation, and operational handover materials while supporting production releases under enterprise change-control and cloud-governance processes.

Design, build, and maintain high-performance backend systems and scalable APIs using Python and FastAPI. Integrate AI-driven solutions (Generative/Agentic AI), work with Databricks and SQL/RDBMS, collaborate with cross-functional teams, optimize performance, and contribute to architecture and technical decisions.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Develop and maintain Adobe Experience Manager applications, components, templates, workflows, and integrations. Build Java backend services, REST APIs, and responsive Angular frontends. Troubleshoot application and performance issues, perform testing, participate in code reviews and Agile ceremonies, support deployments, document solutions, and collaborate with product, QA, UX/UI, and technical teams.

Develop and maintain Oracle APEX applications, Oracle Analytics Server/OBIEE reporting solutions, BI Publisher reports, and Oracle database objects. Build dashboards, workflows, integrations, REST APIs, and data models; write complex SQL and PL/SQL; troubleshoot and tune application and database performance; support deployments, testing, production issues, documentation, and stakeholder collaboration.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Owns end-to-end automated quality engineering for an AI agent development platform. Responsibilities include frontend and backend automation, Temporal workflow reliability testing, multi-tenant isolation, sandbox and security testing, statistical LLM evaluation, adversarial test corpora, HITL validation, performance and concurrency testing, observability assertions, CI/CD release gates, evidence reporting, synthetic data pipelines, and mentoring QA engineers.

Develop, customize, integrate, test, deploy, and support Dynamics 365 Business Central solutions using AL and extensions. Collaborate with cross-functional teams to translate business requirements into technical designs, integrate third-party systems through APIs and web services, document solutions, maintain coding standards, support upgrades, tune performance, and diagnose operational issues. The role also involves Power Platform capabilities, reporting, database work, CI/CD tools, and specialized Business Central workflow and finance integrations.

Own and prioritize delivery backlogs for a connected consumer grooming ecosystem spanning mobile apps, IoT devices, firmware, BLE, hardware, UX, and cloud services. Translate approved Epics into user stories, acceptance criteria, and sprint-ready work. Coordinate dependencies, compliance, releases, risks, documentation, and distributed engineering teams while serving as the primary delivery liaison between Product Management and technical partners.

Lead a 10–15-person engineering team building agentic AI systems from the ground up. Set architecture strategy for planning, memory, tool use, multi-agent coordination, guardrails, observability, evaluations, reliability, and safety. Drive adoption of an AI-Driven Development Lifecycle, partner with product managers on ambiguous requirements, oversee delivery and technical reviews, and support hiring, mentorship, career growth, and leadership reporting.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Gather and document business and functional requirements for card and payment solutions. Collaborate with stakeholders, product owners, engineering, QA, and processors across the SDLC. Define user stories, process flows, acceptance criteria, and technical specifications; analyze impacts and dependencies; support UAT, integration testing, defect triage, releases, and production issues. Apply expertise in card processing, payment networks, ISO 8583, tokenization, digital wallets, chargebacks, reconciliation, and regulatory compliance.